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WATB: Wild Animal Tracking Benchmark

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Abstract

With the development of computer vision technology, many advanced computer vision methods have been successfully applied to animal detection, tracking, recognition and behavior analysis, which is of great help to ecological protection, biodiversity conservation and environmental protection. As existing datasets applied to target tracking contain various kinds of common objects, but rarely focus on wild animals, this paper proposes the first benchmark, named Wild Animal Tracking Benchmark (WATB), to encourage further progress of research and applications of visual object tracking. WATB contains more than 203,000 frames and 206 video sequences, and covers different kinds of animals from land, sea and sky. The average length of the videos is over 980 frames. Each video is manually labelled with thirteen challenge attributes including illumination variation, rotation, deformation, and so on. In the dataset, all frames are annotated with axis-aligned bounding boxes. To reveal the performance of these existing tracking algorithms and provide baseline results for future research on wild animal tracking, we benchmark a total of 38 state-of-the-art trackers and rank them according to tracking accuracy. Evaluation results demonstrate that the trackers based on deep networks perform much better than other trackers like correlation filters. Another finding on the basis of the evaluation results is that wild animals tracking is still a big challenge in computer vision community. The benchmark WATB and evaluation results are released on the project website https://w-1995.github.io/.

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Availability of Data and Materials

The datasets generated during and/or analysed during the current study are available in the project website: https://w-1995.github.io/.

Notes

  1. For the abbreviations, please refer to Table 7 in the supplementary part.

  2. http://graph.baidu.com/.

References

  • Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., & Torr, P.H. (2016). Staple: Complementary learners for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401–1409. IEEE, Las Vegas, USA. https://doi.org/10.1109/CVPR.2016.156

  • Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., & Torr, P.H. (2016). Fully-convolutional siamese networks for object tracking. In: European Conference on Computer Vision, pp. 850–865. Springer, Amsterdam, Netherlands. https://doi.org/10.1007/978-3-319-48881-3_56

  • Bhat, G., Danelljan, M., Gool, L.V., & Timofte, R. (2019). Learning discriminative model prediction for tracking. In: IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, pp. 6182–6191. https://doi.org/10.1109/ICCV.2019.00628

  • Bolme, D.S., Beveridge, J.R., Draper, B.A., & Lui, Y.M. (2010). Visual object tracking using adaptive correlation filters. In: International Conference on Computer Vision and Pattern Recognition, pp. 2544–2550. IEEE, San Francisco, USA. https://doi.org/10.1109/CVPR.2010.5539960

  • Cao, Z., Fu, C., Ye, J., Li, B., & Li, Y. (2021). Hift: Hierarchical feature transformer for aerial tracking. In: IEEE/CVF Conference on Computer Vision, pp. 15457–15466. IEEE/CVF, Montreal, QC, Canada. https://doi.org/10.1109/ICCV48922.2021.01517

  • Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., & Lu, H. (2021). Transformer tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 8126–8135. https://doi.org/10.1109/CVPR46437.2021.00803

  • Chen, Z., Zhong, B., Li, G., Zhang, S., & Ji, R. (2020). Siamese box adaptive network for visual tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6668–6677. IEEE/CVF, Seattle, WA, USA. https://doi.org/10.1109/CVPR42600.2020.00670

  • Dai, K., Wang, D., Lu, H., Sun, C., & Li, J. (2019). Visual tracking via adaptive spatially-regularized correlation filters. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4670–4679. IEEE/CVF, Salt Lake City, USA. https://doi.org/10.1109/CVPR.2019.00480

  • Danelljan, M., Bhat, G., Khan, F.S., & Felsberg, M. (2019). Atom: Accurate tracking by overlap maximization. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, pp. 4660–4669. https://doi.org/10.1109/CVPR.2019.00479

  • Danelljan, M., Bhat, G., Shahbaz Khan, F., & Felsberg, M. (2017). Eco: Efficient convolution operators for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6638–6646. IEEE, Honolulu, Hawaii. https://doi.org/10.1109/CVPR.2017.733

  • Danelljan, M., Häger, G., Khan, F., & Felsberg, M. (2014). Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference. Bmva Press, Nottingham, England. https://doi.org/10.5244/C.28.65

  • Danelljan, M., Hager, G., Shahbaz Khan, F., & Felsberg, M. (2015). Learning spatially regularized correlation filters for visual tracking. In: IEEE International Conference on Computer Vision, pp. 4310–4318. IEEE, Santiago, Chile. https://doi.org/10.1109/ICCV.2015.490

  • Danelljan, M., Häger, G., Khan, F. S., & Felsberg, M. (2016). Discriminative scale space tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(8), 1561–1575. https://doi.org/10.1109/TPAMI.2016.2609928.

    Article  Google Scholar 

  • Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019). Lasot: A high-quality benchmark for large-scale single object tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5374–5383. IEEE/CVF, Long Beach, USA. https://doi.org/10.1109/CVPR.2019.00552

  • Fan, H., Miththanthaya, H.A., Harshit, Rajan, S.R., Liu, X., Zhou, Z., Lin, Y., & Ling, H. (2021). Transparent object tracking benchmark. In: International Conference on Computer Vision, pp. 10734–10743. IEEE/CVF, Nashville, TN, USA. https://doi.org/10.1109/ICCV48922.2021.01056

  • Feng, W., Han, R., Guo, Q., Zhu, J., & Wang, S. (2019). Dynamic saliency-aware regularization for correlation filter-based object tracking. IEEE Transactions on Image Processing, 28(7), 3232–3245. https://doi.org/10.1109/TIP.2019.2895411.

    Article  MathSciNet  MATH  Google Scholar 

  • Fu, Z., Liu, Q., Fu, Z., & Wang, Y. (2021). Stmtrack: Template-free visual tracking with space-time memory networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 13774–13783. https://doi.org/10.1109/CVPR46437.2021.01356

  • Fukunaga, T., Kubota, S., Oda, S., & Iwasaki, W. (2015). Grouptracker: Video tracking system for multiple animals under severe occlusion. Computational Biology and Chemistry, 57, 39–45. https://doi.org/10.1016/j.compbiolchem.2015.02.006.

    Article  Google Scholar 

  • Fu, C., Xu, J., Lin, F., Guo, F., Liu, T., & Zhang, Z. (2020). Object saliency-aware dual regularized correlation filter for real-time aerial tracking. IEEE Transactions on Geoscience and Remote Sensing, 58(12), 8940–8951. https://doi.org/10.1109/TGRS.2020.2992301.

    Article  Google Scholar 

  • Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., & Lucey, S. (2017). Need for speed: A benchmark for higher frame rate object tracking. In: International Conference on Computer Vision, pp. 1134–1143. IEEE, Venice, Italy https://doi.org/10.1109/ICCV.2017.128

  • Guo, D., Shao, Y., Cui, Y., Wang, Z., Zhang, L., & Shen, C. (2021). Graph attention tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 9543–9552. https://doi.org/10.1109/CVPR46437.2021.00942

  • Guo, D., Wang, J., Cui, Y., Wang, Z., & Chen, S. (2020). Siamcar: Siamese fully convolutional classification and regression for visual tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6269–6277. IEEE/CVF, Seattle, WA, USA. https://doi.org/10.1109/CVPR42600.2020.00630

  • Haalck, L., Mangan, M., Webb, B., & Risse, B. (2020). Towards image-based animal tracking in natural environments using a freely moving camera. Journal of Neuroscience Methods, 330, 108455. https://doi.org/10.1016/j.jneumeth.2019.108455.

    Article  Google Scholar 

  • He, Z., Fan, Y., Zhuang, J., Dong, Y., & Bai, H. (2017). Correlation filters with weighted convolution responses. In: IEEE International Conference on Computer Vision Workshop, pp. 1992–2000. IEEE, Venice, Italy. https://doi.org/10.1109/ICCVW.2017.233

  • Henriques, J.F., Caseiro, R., Martins, P., & Batista, J. (2012). Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715. Springer, Florence, Italy. https://doi.org/10.1007/978-3-642-33765-9_50

  • Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2014). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 583–596. https://doi.org/10.1109/TPAMI.2014.2345390.

    Article  Google Scholar 

  • Huang, Z., Fu, C., Li, Y., Lin, F., & Lu, P. (2019). Learning aberrance repressed correlation filters for real-time uav tracking. In: IEEE/CVF International Conference on Computer Vision, pp. 2891–2900. IEEE/CVF, Seoul, Korea(south). https://doi.org/10.1109/ICCV.2019.00298

  • Huang, L., Zhao, X., & Huang, K. (2021). Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5), 1562–1577. https://doi.org/10.1109/TPAMI.2019.2957464.

    Article  Google Scholar 

  • Kiani Galoogahi, H., Fagg, A., & Lucey, S. (2017). Learning background-aware correlation filters for visual tracking. In: IEEE International Conference on Computer Vision, pp. 1135–1143. IEEE, Venice, Italy. https://doi.org/10.1109/ICCV.2017.129

  • Li, Y., & Zhu, J. (2014). A scale adaptive kernel correlation filter tracker with feature integration. In: European Conference on Computer Vision Workshop, pp. 254–265. Springer, Zurich, Switzerland. https://doi.org/10.1007/978-3-319-16181-5_18

  • Li, Y., Fu, C., Ding, F., Huang, Z., & Lu, G. (2020). Autotrack: Towards high-performance visual tracking for uav with automatic spatio-temporal regularization. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11923–11932. IEEE/CVF, Seattle, WA, USA. https://doi.org/10.1109/CVPR42600.2020.01194

  • Li, F., Tian, C., Zuo, W., Zhang, L., & Yang, M.-H. (2018). Learning spatial-temporal regularized correlation filters for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4904–4913. IEEE, Salt Lake City, USA. https://doi.org/10.1109/CVPR.2018.00515

  • Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J. (2019). Siamrpn++: Evolution of siamese visual tracking with very deep networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4282–4291. IEEE/CVF, Long Beach, USA. https://doi.org/10.1109/CVPR.2019.00441

  • Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018). High performance visual tracking with siamese region proposal network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971–8980. IEEE, Salt Lake City, USA. https://doi.org/10.1109/CVPR.2018.00935

  • Liang, P., Blasch, E., & Ling, H. (2015). Encoding color information for visual tracking: Algorithms and benchmark. IEEE Transactions on Image Processing, 24(12), 5630–5644. https://doi.org/10.1109/TIP.2015.2482905.

    Article  MathSciNet  MATH  Google Scholar 

  • Li, C., Liang, X., Lu, Y., Zhao, N., & Tang, J. (2019). Rgb-t object tracking: Benchmark and baseline. Pattern Recognition, 96, 106977. https://doi.org/10.1016/j.patcog.2019.106977.

    Article  Google Scholar 

  • Lin, Y., Cheng, S., Shen, J., & Pantic, M. (2019). Mobiface: a novel dataset for mobile face tracking in the wild. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–8. https://doi.org/10.1109/FG.2019.8756581

  • Liu, S., Liu, D., Srivastava, G., Polap, D., & Woźniak, M. (2021). Overview of correlation filter based algorithms in object tracking. Complex and Intelligent Systems, 7, 1895–1917. https://doi.org/10.1007/s40747-020-00161-4.

    Article  Google Scholar 

  • Li, P., Wang, D., Wang, L., & Lu, H. (2018). Deep visual tracking: Review and experimental comparison. Pattern Recognition, 76, 323–338. https://doi.org/10.1016/j.patcog.2017.11.007.

    Article  Google Scholar 

  • Li, F., Wu, X., Zuo, W., Zhang, D., & Zheng, L. (2020). Remove cosine window from correlation filter-based visual trackers: When and how. IEEE Transactions on Image Processing, 29, 7045–7060. https://doi.org/10.1109/TIP.2020.2997521.

    Article  MathSciNet  MATH  Google Scholar 

  • Lopez-Marcano, S., Jinks, E. L., Buelow, C. A., Brown, C. J., Wang, D., Kusy, B., et al. (2021). Automatic detection of fish and tracking of movement for ecology. Ecology and Evolution, 11, 8254–8263. https://doi.org/10.1002/ece3.7656.

    Article  Google Scholar 

  • Lu, H., & Wang, D. (2019). Online Visual Tracking. Singapore: Springer.

    Book  Google Scholar 

  • Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (2015). Hierarchical convolutional features for visual tracking. In: IEEE International Conference on Computer Vision, pp. 3074–3082. IEEE, Santiago, Chile https://doi.org/10.1109/ICCV.2015.352

  • Mathis, A., Mamidanna, P., Cury, K.M., Abe, T., N., M.V., W., M.M., & Bethge, M. (2018). Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience 21, 1281–1289. https://doi.org/10.1038/s41593-018-0209-y

  • Mueller, M., Smith, N., & Ghanem, B. (2016). A benchmark and simulator for uav tracking. In: European Conference on Computer Vision, pp. 445–461. Springer, Amsterdam, The Netherlands. https://doi.org/10.1007/978-3-319-46448-0_27

  • Nam, H., & Han, B. (2016). Learning multi-domain convolutional neural networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302. IEEE, Las Vegas, USA. https://doi.org/10.1109/CVPR.2016.465

  • Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., & Palmer, M. S. (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences of the United States of America, 115(25), 5716–5725. https://doi.org/10.1073/pnas.1719367115.

    Article  Google Scholar 

  • Ravoor, P. C., & Sudarshan, T. S. B. (2020). Deep learning methods for multi-species animal re-identification and tracking-a survey. Computer Science Review, 38, 100289. https://doi.org/10.1016/j.cosrev.2020.100289.

    Article  MathSciNet  Google Scholar 

  • Risse, B., Mangan, M., Del Pero, L., & Webb, B. (2017). Visual tracking of small animals in cluttered natural environments using a freely moving camera. In: 2017 IEEE International Conference on Computer Vision Workshops, pp. 2840–2849. IEEE/CVF, Venice, Italy. https://doi.org/10.1109/ICCVW.2017.335

  • Risse, B., Mangan, M., Del Pero, K., & Webb, B. (2017). Visual tracking of small animals in cluttered natural environments using a freely moving camera. In: International Conference on Computer Vision Workshop, pp. 2840–2849. https://doi.org/10.1016/j.anbehav.2016.12.005

  • Shen, J., Zafeiriou, S., Chrysos, G.G., Kossaifi, J., Tziiropoulos, G., & Pantic, M. (2015). The first facial landmark tracking in-the-wild challenge: benchmark and results. In: International Conference on Computer Vision Workshop, pp. 1003–1011. IEEE, Santiago, Chile. https://doi.org/10.1109/ICCVW.2015.132

  • Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. .R., Zuffi, S., Risse, B., et al. (2022). Perspectives in machine learning for wildlife conservation. Nature Communication, 13(792), 1–15. https://doi.org/10.1038/s41467-022-27980-y.

    Article  Google Scholar 

  • Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017). Applications of machine learning in animal behaviour studies. Animal Behaviour, 124, 203–220. https://doi.org/10.1016/j.anbehav.2016.12.005.

    Article  Google Scholar 

  • Valmadre, J., Bertinetto, L., Henriques, J.F., Tao, R., Vedaldi, A., Smeulders, A.W.M., Torr, P.H.S., & Gavves, E. (2018). Long-term tracking in the wild: A benchmark. In: European Conference on Computer Vision, pp. 692–707. Springer, Munich, Germany. https://doi.org/10.1007/978-3-030-01219-9_41

  • Van der Zande, L. E., Guzhva, O., & Rodenburg, T. B. (2021). Individual detection and tracking of group housed pigs in their home pen using computer vision. Frontiers in Animal Science, 2, 669312. https://doi.org/10.3389/fanim.2021.669312.

    Article  Google Scholar 

  • Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015). Visual tracking with fully convolutional networks. In: IEEE International Conference on Computer Vision, pp. 3119–3127. IEEE, Santiago, Chile. https://doi.org/10.1109/ICCV.2015.357

  • Wang, Q., Zhang, L., Bertinetto, L., Hu, W., & Torr, P.H.S. (2019). Fast online object tracking and segmentation: A unifying approach. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, pp. 1328–1338. https://doi.org/10.1109/CVPR.2019.00142

  • Wang, F., Wang, C., Yin, S., He, J., Sun, F., & Zhang, J. (2022). Amtset: A benchmark for abrupt motion tracking. Multimedia Tools and Applications, 81, 4711–4734. https://doi.org/10.1007/s11042-021-10947-4.

    Article  Google Scholar 

  • Weinstein, B. G. (2018). A computer vision for animal ecology. Journal of Animal Ecology, 87, 533–545. https://doi.org/10.1111/1365-2656.12780.

    Article  Google Scholar 

  • Wu, Y., Lim, J., & Yang, M.-H. (2013). Online object tracking: A benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418. IEEE, Portland, USA. https://doi.org/10.1109/CVPR.2013.312

  • Wu, Y., Lim, J., & Yang, M. H. (2015). Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1834–1848. https://doi.org/10.1109/TPAMI.2014.2388226.

    Article  Google Scholar 

  • Xiong, F., Zhou, J., & Qian, Y. (2020). Material based object tracking in hyperspectral videos. IEEE Transactions on Image Processing, 29, 3719–3733. https://doi.org/10.1109/TIP.2020.2965302.

    Article  MATH  Google Scholar 

  • Xu, T., Feng, Z., Wu, X., & Kittler, J. (2019). Joint group feature selection and discriminative filter learning for robust visual object tracking. In: IEEE/CVF International Conference on Computer Vision, pp. 7950–7960. IEEE/CVF, Seoul, Korea. https://doi.org/10.1109/ICCV.2019.00804

  • Xu, T., Feng, Z.-H., Wu, X.-J., & Kittler, J. (2019). Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking. IEEE Transactions on Image Processing, 28(11), 5596–5609. https://doi.org/10.1109/TIP.2019.2919201.

    Article  MathSciNet  MATH  Google Scholar 

  • Xu, T., Feng, Z., Wu, X., & Kittler, J. (2021). Adaptive channel selection for robust visual object tracking with discriminative correlation filters. International Journal of Computer Vision, 129, 1359–1375. https://doi.org/10.1007/s11263-021-01435-1.

    Article  MATH  Google Scholar 

  • Yan, B., Peng, H., Fu, J., Wang, D., & Lu, H. (2021). Learning spatio-temporal transformer for visual tracking. In: IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, pp. 10448–10457. https://doi.org/10.1109/ICCV48922.2021.01028

  • Ye, J., Fu, C., Lin, F., Ding, F., An, S., & Lu, G. (2022). Multi-regularized correlation filter for uav tracking and self-localization. IEEE Transactions on Industrial Electronics, 69(6), 6004–6014. https://doi.org/10.1109/TIE.2021.3088366.

    Article  Google Scholar 

  • Yu, Y.a. (2020). Deformable siamese attention networks for visual object tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6728–6737. IEEE/CVF, Seattle, WA, USA. https://doi.org/10.1109/CVPR42600.2020.00676

  • Zheng, G., Fu, C., Ye, J., Lin, F., & Ding, F. (2021). Mutation sensitive correlation filter for real-time uav tracking with adaptive hybrid label. In: IEEE International Conference on Robotics and Automation, pp. 503–509. IEEE, Xi’an, China. https://doi.org/10.1109/ICRA48506.2021.9561931

  • Zuo, W., Wu, X., Lin, L., Zhang, L., & Yang, M.-H. (2018). Learning support correlation filters for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5), 1158–1172. https://doi.org/10.1109/TPAMI.2018.2829180.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61972068 and 61976042, Innovative Talents Program for LiaoningUniversities under Grant LR2019020 and the Liaoning Revitalization Talents Program under Grant XLYC2007023.

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Author notes

  1. P. Cao, F. Li, X. Wang, These authors contributed equally to this work.

Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian Minzu University, No. 18, Liaohe West Road, Dalian, 116600, Liaoning, China

    Fasheng Wang, Ping Cao, Fu Li, Xing Wang, Bing He & Fuming Sun

  2. School of Information Science and Technology, University of Science and Technology of China, No. 96, Jinzhai Road, Hefei, 230026, Anhui, China

    Fasheng Wang

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Contributions

FW and FS conceived this study. FW wrote the initial manuscript, and FS reviewed and edited it. The other four authors took part in the construction of WTAB. PC and FL are responsible for tracker evaluation. XW is responsible for building the project website.

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Correspondence to Fuming Sun.

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Wang, F., Cao, P., Li, F. et al. WATB: Wild Animal Tracking Benchmark. Int J Comput Vis 131, 899–917 (2023). https://doi.org/10.1007/s11263-022-01732-3

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